Sampling-Based Visibility Task Planning

2026-08-02

AI Summary

This work addresses the challenge of incorporating field-of-view constraints—such as those imposed by cameras or directional antennas—into traditional task and motion planning, which often fail to handle visibility requirements effectively. To this end, the authors propose two sampling-based algorithms: VisPRM and VisRRT. VisPRM leverages hierarchical environment modeling and line-of-sight integrity analysis to generate feasible configurations, while VisRRT accelerates exploration through a goal-directed inverse kinematics “glance” mechanism. This study is the first to integrate line-of-sight integrity into hierarchical sampling and introduces a customized inverse kinematics solver, thereby overcoming the limitations of conventional heuristics and distance metrics in visibility-constrained tasks. Experimental results demonstrate that both methods significantly outperform baseline approaches—including RRT, PRM, and VIR—in terms of success rate and computational efficiency across both simulation and physical robotic platforms.

Abstract

Robot Task and Motion Planning (TAMP) algorithms enable autonomous operation by incorporating the specific functions and constraints of end-effector tools, such as grippers or soldering irons, directly into the planning process. In this paper, we explore sampling-based TAMP algorithms specifically designed for a critical subset of devices whose unique properties make traditional planning methods ineffective. Visibility-based instruments, such as exteroceptive sensors, cameras, flashlights and directional antennas, are essential across a vast array of human activities. The unique properties of these devices, and particularly, their field-of-view, render many widely used heuristics and distance metrics less effective. We introduce two new sampling-based algorithms, VisPRM and VisRRT, designed to tackle visibility-based tasks. VisPRM employs a hierarchical decomposition of the environment, leveraging the concept of visibility integrity, to efficiently sample configurations with a clear line-of-sight to the target. A specialized Inverse Kinematics solver enables VisRRT to "glance" in the direction of the target at opportune moments, facilitating the rapid discovery of key configurations. We show that VisPRM and VisRRT achieve a higher success rate and faster runtimes compared to adaptations of RRT, PRM and VIR, through both simulated and physical experiments.

Problem

• Visibility-based planning
• Task and Motion Planning
• Field-of-view constraints
• Sampling-based planning
• Robot perception

Innovation

• visibility-based planning
• sampling-based TAMP
• VisPRM
• VisRRT
• field-of-view constraints